#pragma once

// ${generated_comment}

#include <ATen/ATen.h>
#include <ATen/core/functional.h>
#include <ATen/TensorGeometry.h>

#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/autograd/saved_variable.h>
#include <torch/csrc/Export.h>

#include <c10/core/SymIntArrayRef.h>

using namespace torch::autograd;

namespace at_npu { namespace autograd { namespace generated {

using at::Scalar;
using at::Tensor;
using at::IntArrayRef;
using at::ArrayRef;
using at::Type;
using at::TensorGeometry;
using at::ScalarType;
using c10::optional;
using c10::fmap;

inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs) {
  // NB: we must explicitly do the conversion in the lambda, otherwise template
  // deduction will give a Tensor of Variable which is not convertible
  return fmap(xs, [](const SavedVariable& x) {
    return static_cast<Tensor>(x.unpack());
  });
}

inline c10::List<c10::optional<Tensor>> unpack_opt_list(at::ArrayRef<SavedVariable> xs) {
  torch::List<c10::optional<Tensor>> result;
  result.reserve(xs.size());
  for (const SavedVariable& v : xs) {
    result.push_back(v.unpack());
  }
  return result;
}

struct TypeAndSize {
    TypeAndSize() : options(at::TensorOptions()) {}
    /* implicit */
    TypeAndSize(const Tensor & t)
        : sizes(t.sizes().vec())
        , options(t.options()) {}

  Tensor zeros() { return at::zeros(sizes, options); }

private:
  std::vector<int64_t> sizes;
  at::TensorOptions options;
};

${autograd_function_declarations}

}}} // namespace at_npu::autograd::generated
